Wang Qian, Xiao Xuan, Liu Zi
Department of Computer, Jing-De-Zhen Ceramic University, Jing-De-Zhen, 333403, China.
School of Information Engineering, Jiangxi Arts & Ceramics Technology Institute, Jing-De-Zhen, 333300, China.
Sci Rep. 2024 Dec 30;14(1):32024. doi: 10.1038/s41598-024-83533-x.
Considering the substantial inaccuracies inherent in the traditional manual identification of ceramic categories and the issues associated with analyzing ceramics based on chemical or spectral features, which may lead to the destruction of ceramics, this paper introduces a novel provenance classification of archaeological ceramics which relies on microscopic features and an ensemble deep learning model, overcoming the time consuming and require costly equipment limitations of current standard methods, and without compromising the structural integrity and artistic value of ceramics. The proposed model includes the following: the construction of a dataset for ancient ceramic microscopic images, image preprocessing methods based on Gamma correction and CLAHE equalization algorithms, extraction of image features based on three deep learning architectures-VGG-16, Inception-v3 and GoogLeNet, and optimal fusion. This latter is based on stochastic gradient descent (SGD) algorithm, which allows optimal fitting of the fusion model parameters by freezing and unfreezing model layers. The experiments employ accuracy, precision, recall and F1 score criteria to offer a comprehensive of the classification outcomes. Under 5-fold cross-validation and independent testing, the proposed fusion-based model performed excellently after comparing above three typical deep learning model. The predictive results of the ensemble deep learning are very stable at about 0.9601, 0.9615, 0.9607 and 0.9583 in precision, recall, F1-score, and accuracy on the independent testing dataset, respectively. This indicates that our model is robust and reliable. Furthermore, we use correspondence analysis to explore the distribution of the ceramic microscopic images from different kilns. This method can be applied in the field of ceramic cultural relic identification, contributing to improved diagnostic accuracy and efficiency, and providing new ideas and methods for related research areas.
考虑到传统手工识别陶瓷类别存在的重大误差，以及基于化学或光谱特征分析陶瓷所涉及的问题（这可能导致陶瓷损坏），本文介绍了一种基于微观特征和集成深度学习模型的考古陶瓷产地分类新方法，克服了当前标准方法耗时且需要昂贵设备的局限性，同时不损害陶瓷的结构完整性和艺术价值。所提出的模型包括以下内容：构建古代陶瓷微观图像数据集、基于伽马校正和对比度受限自适应直方图均衡化（CLAHE）算法的图像预处理方法、基于三种深度学习架构（VGG - 16、Inception - v3和GoogLeNet）提取图像特征以及最优融合。最优融合基于随机梯度下降（SGD）算法，通过冻结和解冻模型层来实现融合模型参数的最优拟合。实验采用准确率、精确率、召回率和F1分数标准来全面评估分类结果。在五折交叉验证和独立测试下，与上述三种典型深度学习模型相比，所提出的基于融合的模型表现出色。在独立测试数据集上，集成深度学习的预测结果在精确率、召回率、F1分数和准确率方面分别非常稳定，约为0.9601、0.9615、0.9607和0.9583。这表明我们的模型强大且可靠。此外，我们使用对应分析来探索来自不同窑口的陶瓷微观图像分布。该方法可应用于陶瓷文物鉴定领域，有助于提高诊断的准确性和效率，并为相关研究领域提供新的思路和方法。
